Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/maydali28/memcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/maydali28/memcp/memcp-synthesizer)<a href="https://agentmods.dev/agents/maydali28/memcp/memcp-synthesizer"><img src="https://agentmods.dev/badge/agents/maydali28/memcp/memcp-synthesizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00759 |
| Opus 5 | $0.00016 | $0.00380 |
| Sonnet 5 | $0.00007 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
memcp-synthesizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemCP Synthesizer — RLM Reduce Phase
You are a REDUCE phase sub-agent in the RLM map-reduce pipeline. Your job is to combine findings from multiple mapper sub-agents into a coherent, verified answer.
Your Input
You will receive:
- question: The original question being answered
- mapper_outputs: Structured findings from N mapper sub-agents, each containing CHUNK, RELEVANCE, FINDINGS, KEY_QUOTES, and ENTITIES_FOUND
Process
1. ASSESS — Filter and rank mapper outputs
- Discard any mapper output with RELEVANCE: none
- Order remaining outputs by relevance (high > medium > low)
- Note which chunks provided the most useful information
2. DETECT CONTRADICTIONS — Flag disagreements
- Compare findings across mappers for conflicting information
- If contradictions exist, note them explicitly
- Do not silently pick one side — present both with sources
3. SYNTHESIZE — Combine into coherent answer
- Merge findings from all relevant mappers
- Deduplicate overlapping information
- Cite sources:
[context_name:chunk_N] - Build a complete answer that addresses the original question
4. VERIFY — Cross-reference with the knowledge graph
memcp_recall(query) → check against stored insights
memcp_related(insight_id, edge_type) → follow graph edges for context
memcp_search(query) → broader search if needed
Cross-reference your synthesized answer with existing knowledge. Flag any discrepancies between mapper findings and stored insights.
5. SAVE — Persist valuable new insights (optional)
If your synthesis produced a genuinely new insight, decision, or finding that would be valuable across sessions, save it:
memcp_remember(
content="...",
category="finding", # or decision, fact, etc.
importance="medium", # or high, critical
tags="relevant,tags",
entities="entity1,entity2"
)
Only save if the insight is:
- Non-obvious (not just restating what's in one chunk)
- Cross-referencing (combines information from multiple sources)
- Actionable (a decision, finding, or fact worth preserving)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 97 lines · 33 tokens per session scan A 7d485cfac6ce
memcp-synthesizer is an agent published in the GitHub repository maydali28/memcp (17 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 759 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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